Model comparison
Llama 4 Scout vs Qwen3.8 Max
Qwen3.8 Max is the stronger model overall, scoring 56.8 to 27.7 on the Noometry Index. Llama 4 Scout costs 20× less per token, which makes it the better buy when Qwen3.8 Max's lead doesn't matter for your workload.
Last verified . 25 shared benchmarks.
Meta
Alibaba (Qwen)Summary
- They share 25 benchmarks with published results for both. Llama 4 Scout scores higher in 0 categories and Qwen3.8 Max in 10 categories; 10 gaps are clear of the uncertainty.
- The widest gap is in math, where Qwen3.8 Max leads 73.2 to 19.6.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 7.8% for Llama 4 Scout and 100% for Qwen3.8 Max.
- Llama 4 Scout is cheaper at $0.10 / $0.30 per million input/output tokens, against $2 / $6 for Qwen3.8 Max.
- Qwen3.8 Max accepts more context: 1M tokens versus 128K.
- Llama 4 Scout has downloadable open weights; the other is API-only.
Side by side
| Llama 4 Scout | Qwen3.8 Max | |
|---|---|---|
| Provider | Meta | Alibaba (Qwen) |
| Noometry Index | 27.7 | 56.8 |
| Released | 2025-04-05 | 2026-08-02 |
| Weights | Open | Proprietary |
| Context window | 128K | 1M |
| Max output | 4K | 131K |
| Input $ / M tokens | $0.10 | $2 |
| Output $ / M tokens | $0.30 | $6 |
| Results tracked | 43 | 39 |
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Category by category
Coding Qwen3.8 Max leads
Llama 4 Scout: 20.2 (#339), Qwen3.8 Max: 53.5 (#29)
| Benchmark | Llama 4 Scout | Qwen3.8 Max |
|---|---|---|
| SciCode | 17% | 53.2% |
| LMArena Coding | 1286 | 1502 |
| DeepSWE | — | 57.5% |
| SWE-bench Verified (bash only) | 9.1% | — |
| LMArena WebDev | — | 1674 |
| FrontierSWE | — | 17.8% |
| BigCodeBench Complete | 43.1% | — |
Agentic & Tool Use Qwen3.8 Max leads
Llama 4 Scout: 24.6 (#119), Qwen3.8 Max: 45.4 (#14)
| Benchmark | Llama 4 Scout | Qwen3.8 Max |
|---|---|---|
| APEX-Agents | — | 63.3% |
| Berkeley Function Calling Leaderboard | 28.1% | — |
| τ²-bench Banking | — | 55.1% |
| GDP.pdf | — | 23.2% |
Reasoning Qwen3.8 Max leads
Llama 4 Scout: 9.1 (#345), Qwen3.8 Max: 54.4 (#26)
| Benchmark | Llama 4 Scout | Qwen3.8 Max |
|---|---|---|
| CritPt | 0% | 20% |
| LMArena Hard Prompts | 1266 | 1496 |
| DTBench | 57.9% | 92% |
| LMCA | 12% | 46.2% |
| Epoch Capabilities Index | 129.64 | 156.41 |
| ARC-AGI-2 | 0% | — |
| Kagi LLM Benchmark | 36.9% | — |
| NYT Connections (extended) | — | 88.3% |
| ARC-AGI-1 | 0.5% | — |
| Chess Puzzles | — | 40% |
| Mystery Game Puzzles | — | 38% |
| ForecastBench | 57.5 | — |
Math Qwen3.8 Max leads
Llama 4 Scout: 19.6 (#286), Qwen3.8 Max: 73.2 (#20)
| Benchmark | Llama 4 Scout | Qwen3.8 Max |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 7.8% | 100% |
| LMArena Math | 1287 | 1499 |
| FrontierMath (Tiers 1-3) | — | 74.7% |
| FrontierMath Tier 4 | — | 46.3% |
| ProofBench | — | 58% |
| Omni-MATH | 37.3% | — |
| MATH Level 5 | 62.3% | — |
| FrontierMath (Feb 2025 set) | 0% | — |
Knowledge Qwen3.8 Max leads
Llama 4 Scout: 31.9 (#217), Qwen3.8 Max: 61.7 (#27)
| Benchmark | Llama 4 Scout | Qwen3.8 Max |
|---|---|---|
| GPQA Diamond | 51.8% | 92.7% |
| LMArena Expert | 1235 | 1507 |
| SimpleQA Verified | — | 47.3% |
| MMLU-Pro | 74.2% | — |
| Vectara Hallucination Rate | 7.7% | — |
| GPQA (HELM) | 50.7% | — |
Multimodal Qwen3.8 Max leads
Llama 4 Scout: 32.2 (#102), Qwen3.8 Max: 37.2 (#75)
| Benchmark | Llama 4 Scout | Qwen3.8 Max |
|---|---|---|
| LMArena Vision | 1118 | 1314 |
| Furniture Assembly | — | 20% |
| SpatialViz-Bench | 34.2% | — |
Multilingual Qwen3.8 Max leads
Llama 4 Scout: 41.0 (#212), Qwen3.8 Max: 56.7 (#18)
| Benchmark | Llama 4 Scout | Qwen3.8 Max |
|---|---|---|
| LMArena Non-English | 1252 | 1472 |
| LMArena Chinese | 1255 | 1538 |
| LMArena French | 1282 | 1503 |
| LMArena German | 1272 | 1483 |
| LMArena Japanese | 1206 | 1467 |
| LMArena Korean | 1207 | 1461 |
| LMArena Russian | 1263 | 1481 |
| LMArena Spanish | 1278 | 1492 |
Instruction Following Qwen3.8 Max leads
Llama 4 Scout: 65.8 (#217), Qwen3.8 Max: 77.6 (#17)
| Benchmark | Llama 4 Scout | Qwen3.8 Max |
|---|---|---|
| LMArena Instruction Following | 1248 | 1479 |
| IFEval | 81.8% | — |
Long Context Qwen3.8 Max leads
Llama 4 Scout: 27.5 (#294), Qwen3.8 Max: 45.6 (#31)
| Benchmark | Llama 4 Scout | Qwen3.8 Max |
|---|---|---|
| LMArena Longer Query | 1265 | 1489 |
| Fiction.LiveBench | 36% | — |
Writing & Preference Qwen3.8 Max leads
Llama 4 Scout: 37.0 (#261), Qwen3.8 Max: 67.1 (#30)
| Benchmark | Llama 4 Scout | Qwen3.8 Max |
|---|---|---|
| LMArena Text | 1279 | 1483 |
| LMArena Creative Writing | 1249 | 1479 |
| LMArena Multi-Turn | 1280 | 1489 |
| EQ-Bench Creative Writing | 783 | — |
| WildBench | 78% | — |
Frequently asked questions
Is Llama 4 Scout better than Qwen3.8 Max?
Qwen3.8 Max is the stronger model overall, scoring 56.8 to 27.7 on the Noometry Index. Llama 4 Scout costs 20× less per token, which makes it the better buy when Qwen3.8 Max's lead doesn't matter for your workload.
Which is cheaper, Llama 4 Scout or Qwen3.8 Max?
Llama 4 Scout is cheaper. It lists at $0.10 per million input tokens and $0.30 per million output tokens; Qwen3.8 Max lists at $2 and $6.
Is Llama 4 Scout or Qwen3.8 Max better for coding?
Qwen3.8 Max scores higher on coding benchmarks: 53.5 versus 20.2 in the Noometry coding category.
Which has the bigger context window?
Qwen3.8 Max does, with 1M tokens against 128K.
How many benchmarks do Llama 4 Scout and Qwen3.8 Max share?
25 benchmarks have published results for both models. Llama 4 Scout has 43 scored results on Noometry and Qwen3.8 Max has 39.